Overview

Dataset statistics

Number of variables67
Number of observations2561
Missing cells49703
Missing cells (%)29.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory536.0 B

Variable types

Numeric18
Categorical18
Boolean30
Unsupported1

Warnings

HP1_ADDON_PRE_REN has constant value "False" Constant
QUOTE_DATE has a high cardinality: 495 distinct values High cardinality
COVER_START has a high cardinality: 854 distinct values High cardinality
P1_DOB has a high cardinality: 1768 distinct values High cardinality
MTA_DATE has a high cardinality: 205 distinct values High cardinality
Police has a high cardinality: 2561 distinct values High cardinality
df_index is highly correlated with iHigh correlation
MTA_FAP is highly correlated with LAST_ANN_PREM_GROSSHigh correlation
LAST_ANN_PREM_GROSS is highly correlated with MTA_FAPHigh correlation
i is highly correlated with df_indexHigh correlation
QUOTE_DATE has 1282 (50.1%) missing values Missing
COVER_START has 666 (26.0%) missing values Missing
CLAIM3YEARS has 659 (25.7%) missing values Missing
P1_EMP_STATUS has 666 (26.0%) missing values Missing
P1_PT_EMP_STATUS has 2545 (99.4%) missing values Missing
BUS_USE has 666 (26.0%) missing values Missing
CLERICAL has 2529 (98.8%) missing values Missing
AD_BUILDINGS has 666 (26.0%) missing values Missing
RISK_RATED_AREA_B has 1158 (45.2%) missing values Missing
NCD_GRANTED_YEARS_B has 666 (26.0%) missing values Missing
AD_CONTENTS has 666 (26.0%) missing values Missing
RISK_RATED_AREA_C has 749 (29.2%) missing values Missing
SUM_INSURED_CONTENTS has 666 (26.0%) missing values Missing
NCD_GRANTED_YEARS_C has 666 (26.0%) missing values Missing
CONTENTS_COVER has 666 (26.0%) missing values Missing
BUILDINGS_COVER has 666 (26.0%) missing values Missing
SPEC_SUM_INSURED has 666 (26.0%) missing values Missing
SPEC_ITEM_PREM has 666 (26.0%) missing values Missing
UNSPEC_HRP_PREM has 666 (26.0%) missing values Missing
P1_DOB has 666 (26.0%) missing values Missing
P1_MAR_STATUS has 666 (26.0%) missing values Missing
P1_POLICY_REFUSED has 666 (26.0%) missing values Missing
P1_SEX has 666 (26.0%) missing values Missing
APPR_ALARM has 666 (26.0%) missing values Missing
APPR_LOCKS has 666 (26.0%) missing values Missing
BEDROOMS has 666 (26.0%) missing values Missing
WALL_CONSTRUCTION has 666 (26.0%) missing values Missing
FLOODING has 666 (26.0%) missing values Missing
NEIGH_WATCH has 666 (26.0%) missing values Missing
OCC_STATUS has 666 (26.0%) missing values Missing
OWNERSHIP_TYPE has 666 (26.0%) missing values Missing
PROP_TYPE has 666 (26.0%) missing values Missing
SAFE_INSTALLED has 666 (26.0%) missing values Missing
SEC_DISC_REQ has 666 (26.0%) missing values Missing
SUBSIDENCE has 666 (26.0%) missing values Missing
YEARBUILT has 666 (26.0%) missing values Missing
CAMPAIGN_DESC has 2561 (100.0%) missing values Missing
PAYMENT_METHOD has 666 (26.0%) missing values Missing
LEGAL_ADDON_PRE_REN has 666 (26.0%) missing values Missing
LEGAL_ADDON_POST_REN has 666 (26.0%) missing values Missing
HOME_EM_ADDON_PRE_REN has 666 (26.0%) missing values Missing
HOME_EM_ADDON_POST_REN has 666 (26.0%) missing values Missing
GARDEN_ADDON_PRE_REN has 666 (26.0%) missing values Missing
GARDEN_ADDON_POST_REN has 666 (26.0%) missing values Missing
KEYCARE_ADDON_PRE_REN has 666 (26.0%) missing values Missing
KEYCARE_ADDON_POST_REN has 666 (26.0%) missing values Missing
HP1_ADDON_PRE_REN has 666 (26.0%) missing values Missing
HP1_ADDON_POST_REN has 666 (26.0%) missing values Missing
HP2_ADDON_PRE_REN has 666 (26.0%) missing values Missing
HP2_ADDON_POST_REN has 666 (26.0%) missing values Missing
HP3_ADDON_PRE_REN has 666 (26.0%) missing values Missing
HP3_ADDON_POST_REN has 666 (26.0%) missing values Missing
MTA_FLAG has 659 (25.7%) missing values Missing
MTA_FAP has 1982 (77.4%) missing values Missing
MTA_APRP has 1982 (77.4%) missing values Missing
MTA_DATE has 2302 (89.9%) missing values Missing
LAST_ANN_PREM_GROSS has 659 (25.7%) missing values Missing
POL_STATUS has 666 (26.0%) missing values Missing
WALL_CONSTRUCTION is highly skewed (γ1 = -22.50128421) Skewed
P1_DOB is uniformly distributed Uniform
MTA_DATE is uniformly distributed Uniform
Police is uniformly distributed Uniform
df_index has unique values Unique
i has unique values Unique
Police has unique values Unique
CAMPAIGN_DESC is an unsupported type, check if it needs cleaning or further analysis Unsupported
RISK_RATED_AREA_B has 163 (6.4%) zeros Zeros
NCD_GRANTED_YEARS_B has 449 (17.5%) zeros Zeros
RISK_RATED_AREA_C has 166 (6.5%) zeros Zeros
SUM_INSURED_CONTENTS has 83 (3.2%) zeros Zeros
NCD_GRANTED_YEARS_C has 96 (3.7%) zeros Zeros
SPEC_SUM_INSURED has 1636 (63.9%) zeros Zeros
SPEC_ITEM_PREM has 1637 (63.9%) zeros Zeros
UNSPEC_HRP_PREM has 1390 (54.3%) zeros Zeros
MTA_APRP has 274 (10.7%) zeros Zeros

Reproduction

Analysis started2021-01-25 20:52:38.385311
Analysis finished2021-01-25 20:53:49.653387
Duration1 minute and 11.27 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2561
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129829.3581
Minimum32
Maximum256112
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:49.827397image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile12462
Q166921
median129825
Q3194809
95-th percentile243393
Maximum256112
Range256080
Interquartile range (IQR)127888

Descriptive statistics

Standard deviation74214.84674
Coefficient of variation (CV)0.5716337803
Kurtosis-1.19492662
Mean129829.3581
Median Absolute Deviation (MAD)63944
Skewness-0.0282409952
Sum332492986
Variance5507843477
MonotocityNot monotonic
2021-01-25T20:53:49.985406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539521
 
< 0.1%
2395981
 
< 0.1%
539131
 
< 0.1%
1850541
 
< 0.1%
682521
 
< 0.1%
278551
 
< 0.1%
825911
 
< 0.1%
1053811
 
< 0.1%
1440331
 
< 0.1%
887381
 
< 0.1%
Other values (2551)2551
99.6%
ValueCountFrequency (%)
321
< 0.1%
1391
< 0.1%
1991
< 0.1%
2591
< 0.1%
2801
< 0.1%
ValueCountFrequency (%)
2561121
< 0.1%
2560911
< 0.1%
2559831
< 0.1%
2559611
< 0.1%
2558911
< 0.1%

QUOTE_DATE
Categorical

HIGH CARDINALITY
MISSING

Distinct495
Distinct (%)38.7%
Missing1282
Missing (%)50.1%
Memory size20.1 KiB
11/21/2011
 
10
1/14/2010
 
9
1/11/2011
 
9
1/18/2011
 
9
12/5/2011
 
8
Other values (490)
1234 

Length

Max length10
Median length9
Mean length9.248631744
Min length8

Characters and Unicode

Total characters11829
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)14.6%

Sample

1st row11/5/2011
2nd row1/6/2010
3rd row1/11/2011
4th row1/20/2009
5th row11/28/2007
ValueCountFrequency (%)
11/21/201110
 
0.4%
1/14/20109
 
0.4%
1/11/20119
 
0.4%
1/18/20119
 
0.4%
12/5/20118
 
0.3%
1/20/20118
 
0.3%
11/18/20098
 
0.3%
1/10/20118
 
0.3%
12/16/20108
 
0.3%
1/5/20117
 
0.3%
Other values (485)1195
46.7%
(Missing)1282
50.1%
2021-01-25T20:53:50.309425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/21/201110
 
0.8%
1/11/20119
 
0.7%
1/14/20109
 
0.7%
1/18/20119
 
0.7%
1/20/20118
 
0.6%
12/5/20118
 
0.6%
1/10/20118
 
0.6%
11/18/20098
 
0.6%
12/16/20108
 
0.6%
11/10/20117
 
0.5%
Other values (485)1195
93.4%

Most occurring characters

ValueCountFrequency (%)
13242
27.4%
/2558
21.6%
22364
20.0%
02244
19.0%
9328
 
2.8%
8289
 
2.4%
3212
 
1.8%
7198
 
1.7%
5143
 
1.2%
6128
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9271
78.4%
Other Punctuation2558
 
21.6%

Most frequent character per category

ValueCountFrequency (%)
13242
35.0%
22364
25.5%
02244
24.2%
9328
 
3.5%
8289
 
3.1%
3212
 
2.3%
7198
 
2.1%
5143
 
1.5%
6128
 
1.4%
4123
 
1.3%
ValueCountFrequency (%)
/2558
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11829
100.0%

Most frequent character per script

ValueCountFrequency (%)
13242
27.4%
/2558
21.6%
22364
20.0%
02244
19.0%
9328
 
2.8%
8289
 
2.4%
3212
 
1.8%
7198
 
1.7%
5143
 
1.2%
6128
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11829
100.0%

Most frequent character per block

ValueCountFrequency (%)
13242
27.4%
/2558
21.6%
22364
20.0%
02244
19.0%
9328
 
2.8%
8289
 
2.4%
3212
 
1.8%
7198
 
1.7%
5143
 
1.2%
6128
 
1.1%

COVER_START
Categorical

HIGH CARDINALITY
MISSING

Distinct854
Distinct (%)45.1%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
01/03/2011
 
36
01/02/2011
 
34
01/03/2010
 
28
01/01/2011
 
27
01/12/2010
 
24
Other values (849)
1746 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18950
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique452 ?
Unique (%)23.9%

Sample

1st row10/01/2010
2nd row15/01/2011
3rd row09/11/1999
4th row12/11/2009
5th row22/12/2006
ValueCountFrequency (%)
01/03/201136
 
1.4%
01/02/201134
 
1.3%
01/03/201028
 
1.1%
01/01/201127
 
1.1%
01/12/201024
 
0.9%
01/02/201018
 
0.7%
01/12/200915
 
0.6%
01/01/201014
 
0.5%
28/02/201112
 
0.5%
01/02/200912
 
0.5%
Other values (844)1675
65.4%
(Missing)666
 
26.0%
2021-01-25T20:53:50.606442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/03/201136
 
1.9%
01/02/201134
 
1.8%
01/03/201028
 
1.5%
01/01/201127
 
1.4%
01/12/201024
 
1.3%
01/02/201018
 
0.9%
01/12/200915
 
0.8%
01/01/201014
 
0.7%
01/02/200912
 
0.6%
28/02/201112
 
0.6%
Other values (844)1675
88.4%

Most occurring characters

ValueCountFrequency (%)
05413
28.6%
13837
20.2%
/3790
20.0%
23331
17.6%
9593
 
3.1%
3483
 
2.5%
5358
 
1.9%
8341
 
1.8%
7281
 
1.5%
6271
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15160
80.0%
Other Punctuation3790
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
05413
35.7%
13837
25.3%
23331
22.0%
9593
 
3.9%
3483
 
3.2%
5358
 
2.4%
8341
 
2.2%
7281
 
1.9%
6271
 
1.8%
4252
 
1.7%
ValueCountFrequency (%)
/3790
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18950
100.0%

Most frequent character per script

ValueCountFrequency (%)
05413
28.6%
13837
20.2%
/3790
20.0%
23331
17.6%
9593
 
3.1%
3483
 
2.5%
5358
 
1.9%
8341
 
1.8%
7281
 
1.5%
6271
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII18950
100.0%

Most frequent character per block

ValueCountFrequency (%)
05413
28.6%
13837
20.2%
/3790
20.0%
23331
17.6%
9593
 
3.1%
3483
 
2.5%
5358
 
1.9%
8341
 
1.8%
7281
 
1.5%
6271
 
1.4%

CLAIM3YEARS
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing659
Missing (%)25.7%
Memory size5.1 KiB
False
1693 
True
209 
(Missing)
659 
ValueCountFrequency (%)
False1693
66.1%
True209
 
8.2%
(Missing)659
 
25.7%
2021-01-25T20:53:50.680446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

P1_EMP_STATUS
Categorical

MISSING

Distinct7
Distinct (%)0.4%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
R
1445 
E
391 
S
 
32
H
 
11
N
 
7
Other values (2)
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1895
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowE
5th rowR
ValueCountFrequency (%)
R1445
56.4%
E391
 
15.3%
S32
 
1.2%
H11
 
0.4%
N7
 
0.3%
U6
 
0.2%
V3
 
0.1%
(Missing)666
26.0%
2021-01-25T20:53:50.885458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:50.968463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
r1445
76.3%
e391
 
20.6%
s32
 
1.7%
h11
 
0.6%
n7
 
0.4%
u6
 
0.3%
v3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R1445
76.3%
E391
 
20.6%
S32
 
1.7%
H11
 
0.6%
N7
 
0.4%
U6
 
0.3%
V3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1895
100.0%

Most frequent character per category

ValueCountFrequency (%)
R1445
76.3%
E391
 
20.6%
S32
 
1.7%
H11
 
0.6%
N7
 
0.4%
U6
 
0.3%
V3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin1895
100.0%

Most frequent character per script

ValueCountFrequency (%)
R1445
76.3%
E391
 
20.6%
S32
 
1.7%
H11
 
0.6%
N7
 
0.4%
U6
 
0.3%
V3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1895
100.0%

Most frequent character per block

ValueCountFrequency (%)
R1445
76.3%
E391
 
20.6%
S32
 
1.7%
H11
 
0.6%
N7
 
0.4%
U6
 
0.3%
V3
 
0.2%

P1_PT_EMP_STATUS
Categorical

MISSING

Distinct3
Distinct (%)18.8%
Missing2545
Missing (%)99.4%
Memory size20.1 KiB
E
10 
V
R

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowV
4th rowR
5th rowE
ValueCountFrequency (%)
E10
 
0.4%
V4
 
0.2%
R2
 
0.1%
(Missing)2545
99.4%
2021-01-25T20:53:51.210477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:51.284481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
e10
62.5%
v4
 
25.0%
r2
 
12.5%

Most occurring characters

ValueCountFrequency (%)
E10
62.5%
V4
 
25.0%
R2
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter16
100.0%

Most frequent character per category

ValueCountFrequency (%)
E10
62.5%
V4
 
25.0%
R2
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin16
100.0%

Most frequent character per script

ValueCountFrequency (%)
E10
62.5%
V4
 
25.0%
R2
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII16
100.0%

Most frequent character per block

ValueCountFrequency (%)
E10
62.5%
V4
 
25.0%
R2
 
12.5%

BUS_USE
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1863 
True
 
32
(Missing)
666 
ValueCountFrequency (%)
False1863
72.7%
True32
 
1.2%
(Missing)666
 
26.0%
2021-01-25T20:53:51.331483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

CLERICAL
Boolean

MISSING

Distinct2
Distinct (%)6.2%
Missing2529
Missing (%)98.8%
Memory size5.1 KiB
True
 
27
False
 
5
(Missing)
2529 
ValueCountFrequency (%)
True27
 
1.1%
False5
 
0.2%
(Missing)2529
98.8%
2021-01-25T20:53:51.372486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

AD_BUILDINGS
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1464 
False
431 
(Missing)
666 
ValueCountFrequency (%)
True1464
57.2%
False431
 
16.8%
(Missing)666
26.0%
2021-01-25T20:53:51.413488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

RISK_RATED_AREA_B
Real number (ℝ≥0)

MISSING
ZEROS

Distinct44
Distinct (%)3.1%
Missing1158
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean10.0634355
Minimum0
Maximum97
Zeros163
Zeros (%)6.4%
Memory size20.1 KiB
2021-01-25T20:53:51.505493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q314
95-th percentile25
Maximum97
Range97
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.988634218
Coefficient of variation (CV)0.89319738
Kurtosis13.37963023
Mean10.0634355
Median Absolute Deviation (MAD)6
Skewness2.277568752
Sum14119
Variance80.79554511
MonotocityNot monotonic
2021-01-25T20:53:51.650502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0163
 
6.4%
395
 
3.7%
1090
 
3.5%
888
 
3.4%
183
 
3.2%
778
 
3.0%
1271
 
2.8%
1370
 
2.7%
559
 
2.3%
1157
 
2.2%
Other values (34)549
21.4%
(Missing)1158
45.2%
ValueCountFrequency (%)
0163
6.4%
183
3.2%
234
 
1.3%
395
3.7%
445
 
1.8%
ValueCountFrequency (%)
971
 
< 0.1%
961
 
< 0.1%
476
0.2%
465
0.2%
421
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
1000000.0
1462 
nan
666 
0.0
433 

Length

Max length9
Median length9
Mean length6.425224522
Min length3

Characters and Unicode

Total characters16455
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd row1000000.0
3rd row1000000.0
4th row0.0
5th row1000000.0
ValueCountFrequency (%)
1000000.01462
57.1%
nan666
26.0%
0.0433
 
16.9%
2021-01-25T20:53:51.909517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:51.988521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1000000.01462
57.1%
nan666
26.0%
0.0433
 
16.9%

Most occurring characters

ValueCountFrequency (%)
011100
67.5%
.1895
 
11.5%
11462
 
8.9%
n1332
 
8.1%
a666
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12562
76.3%
Lowercase Letter1998
 
12.1%
Other Punctuation1895
 
11.5%

Most frequent character per category

ValueCountFrequency (%)
n1332
66.7%
a666
33.3%
ValueCountFrequency (%)
011100
88.4%
11462
 
11.6%
ValueCountFrequency (%)
.1895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common14457
87.9%
Latin1998
 
12.1%

Most frequent character per script

ValueCountFrequency (%)
011100
76.8%
.1895
 
13.1%
11462
 
10.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII16455
100.0%

Most frequent character per block

ValueCountFrequency (%)
011100
67.5%
.1895
 
11.5%
11462
 
8.9%
n1332
 
8.1%
a666
 
4.0%

NCD_GRANTED_YEARS_B
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)0.5%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean4.449604222
Minimum0
Maximum9
Zeros449
Zeros (%)17.5%
Memory size20.1 KiB
2021-01-25T20:53:52.068526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.699186119
Coefficient of variation (CV)0.6066126299
Kurtosis-0.950690226
Mean4.449604222
Median Absolute Deviation (MAD)1
Skewness-0.8180380904
Sum8432
Variance7.285605704
MonotocityNot monotonic
2021-01-25T20:53:52.163531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6872
34.0%
0449
17.5%
7275
 
10.7%
5113
 
4.4%
396
 
3.7%
432
 
1.2%
922
 
0.9%
218
 
0.7%
112
 
0.5%
86
 
0.2%
(Missing)666
26.0%
ValueCountFrequency (%)
0449
17.5%
112
 
0.5%
218
 
0.7%
396
 
3.7%
432
 
1.2%
ValueCountFrequency (%)
922
 
0.9%
86
 
0.2%
7275
 
10.7%
6872
34.0%
5113
 
4.4%

AD_CONTENTS
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1814 
False
 
81
(Missing)
666 
ValueCountFrequency (%)
True1814
70.8%
False81
 
3.2%
(Missing)666
 
26.0%
2021-01-25T20:53:52.235535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

RISK_RATED_AREA_C
Real number (ℝ≥0)

MISSING
ZEROS

Distinct36
Distinct (%)2.0%
Missing749
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean8.918322296
Minimum0
Maximum38
Zeros166
Zeros (%)6.5%
Memory size20.1 KiB
2021-01-25T20:53:52.321540image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile23.45
Maximum38
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.598276117
Coefficient of variation (CV)0.851984921
Kurtosis1.25416438
Mean8.918322296
Median Absolute Deviation (MAD)5
Skewness1.141251736
Sum16160
Variance57.73379994
MonotocityNot monotonic
2021-01-25T20:53:52.458548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0166
 
6.5%
5154
 
6.0%
2144
 
5.6%
6113
 
4.4%
1107
 
4.2%
10100
 
3.9%
496
 
3.7%
792
 
3.6%
890
 
3.5%
376
 
3.0%
Other values (26)674
26.3%
(Missing)749
29.2%
ValueCountFrequency (%)
0166
6.5%
1107
4.2%
2144
5.6%
376
3.0%
496
3.7%
ValueCountFrequency (%)
383
 
0.1%
3717
0.7%
342
 
0.1%
321
 
< 0.1%
314
 
0.2%

SUM_INSURED_CONTENTS
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)0.3%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean47976.2533
Minimum0
Maximum100000
Zeros83
Zeros (%)3.2%
Memory size20.1 KiB
2021-01-25T20:53:52.591556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50000
Q150000
median50000
Q350000
95-th percentile50000
Maximum100000
Range100000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10522.61434
Coefficient of variation (CV)0.2193296395
Kurtosis16.79968313
Mean47976.2533
Median Absolute Deviation (MAD)0
Skewness-3.954208012
Sum90915000
Variance110725412.6
MonotocityNot monotonic
2021-01-25T20:53:52.709562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
500001800
70.3%
083
 
3.2%
750007
 
0.3%
600002
 
0.1%
1000002
 
0.1%
700001
 
< 0.1%
(Missing)666
 
26.0%
ValueCountFrequency (%)
083
 
3.2%
500001800
70.3%
600002
 
0.1%
700001
 
< 0.1%
750007
 
0.3%
ValueCountFrequency (%)
1000002
 
0.1%
750007
 
0.3%
700001
 
< 0.1%
600002
 
0.1%
500001800
70.3%

NCD_GRANTED_YEARS_C
Real number (ℝ≥0)

MISSING
ZEROS

Distinct10
Distinct (%)0.5%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean5.50237467
Minimum0
Maximum9
Zeros96
Zeros (%)3.7%
Memory size20.1 KiB
2021-01-25T20:53:52.830569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median6
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.747186502
Coefficient of variation (CV)0.3175331755
Kurtosis3.230840706
Mean5.50237467
Median Absolute Deviation (MAD)0
Skewness-1.836473323
Sum10427
Variance3.052660673
MonotocityNot monotonic
2021-01-25T20:53:52.927575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
61109
43.3%
7323
 
12.6%
5135
 
5.3%
3116
 
4.5%
096
 
3.7%
442
 
1.6%
228
 
1.1%
923
 
0.9%
118
 
0.7%
85
 
0.2%
(Missing)666
26.0%
ValueCountFrequency (%)
096
3.7%
118
 
0.7%
228
 
1.1%
3116
4.5%
442
 
1.6%
ValueCountFrequency (%)
923
 
0.9%
85
 
0.2%
7323
 
12.6%
61109
43.3%
5135
 
5.3%

CONTENTS_COVER
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1464 
False
431 
(Missing)
666 
ValueCountFrequency (%)
True1464
57.2%
False431
 
16.8%
(Missing)666
26.0%
2021-01-25T20:53:52.999579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

BUILDINGS_COVER
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1814 
False
 
81
(Missing)
666 
ValueCountFrequency (%)
True1814
70.8%
False81
 
3.2%
(Missing)666
 
26.0%
2021-01-25T20:53:53.043581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

SPEC_SUM_INSURED
Real number (ℝ≥0)

MISSING
ZEROS

Distinct111
Distinct (%)5.9%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean361.7889182
Minimum0
Maximum18706
Zeros1636
Zeros (%)63.9%
Memory size20.1 KiB
2021-01-25T20:53:53.147587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2518
Maximum18706
Range18706
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1360.932412
Coefficient of variation (CV)3.76167523
Kurtosis51.6938458
Mean361.7889182
Median Absolute Deviation (MAD)0
Skewness6.069495542
Sum685590
Variance1852137.03
MonotocityNot monotonic
2021-01-25T20:53:53.358599image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01636
63.9%
200021
 
0.8%
100014
 
0.5%
300011
 
0.4%
4009
 
0.4%
5009
 
0.4%
3008
 
0.3%
40008
 
0.3%
2008
 
0.3%
25007
 
0.3%
Other values (101)164
 
6.4%
(Missing)666
26.0%
ValueCountFrequency (%)
01636
63.9%
1003
 
0.1%
1191
 
< 0.1%
1201
 
< 0.1%
1401
 
< 0.1%
ValueCountFrequency (%)
187061
< 0.1%
187001
< 0.1%
121901
< 0.1%
115001
< 0.1%
110001
< 0.1%

SPEC_ITEM_PREM
Real number (ℝ≥0)

MISSING
ZEROS

Distinct239
Distinct (%)12.6%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean2.813366755
Minimum0
Maximum195.41
Zeros1637
Zeros (%)63.9%
Memory size20.1 KiB
2021-01-25T20:53:53.545610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.647
Maximum195.41
Range195.41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.53073501
Coefficient of variation (CV)3.743107787
Kurtosis86.28639715
Mean2.813366755
Median Absolute Deviation (MAD)0
Skewness7.299627143
Sum5331.33
Variance110.8963798
MonotocityNot monotonic
2021-01-25T20:53:53.730621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01637
63.9%
15.933
 
0.1%
2.33
 
0.1%
14.332
 
0.1%
1.192
 
0.1%
33.262
 
0.1%
13.162
 
0.1%
12.512
 
0.1%
39.082
 
0.1%
13.362
 
0.1%
Other values (229)238
 
9.3%
(Missing)666
26.0%
ValueCountFrequency (%)
01637
63.9%
0.531
 
< 0.1%
0.711
 
< 0.1%
1.192
 
0.1%
1.311
 
< 0.1%
ValueCountFrequency (%)
195.411
< 0.1%
120.051
< 0.1%
119.121
< 0.1%
103.31
< 0.1%
69.761
< 0.1%

UNSPEC_HRP_PREM
Real number (ℝ≥0)

MISSING
ZEROS

Distinct186
Distinct (%)9.8%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean5.724654354
Minimum0
Maximum68.75
Zeros1390
Zeros (%)54.3%
Memory size20.1 KiB
2021-01-25T20:53:53.913631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.69
95-th percentile27.26
Maximum68.75
Range68.75
Interquartile range (IQR)12.69

Descriptive statistics

Standard deviation10.3245601
Coefficient of variation (CV)1.803525499
Kurtosis2.328141929
Mean5.724654354
Median Absolute Deviation (MAD)0
Skewness1.70054186
Sum10848.22
Variance106.5965413
MonotocityNot monotonic
2021-01-25T20:53:54.105642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01390
54.3%
14.8665
 
2.5%
16.0725
 
1.0%
19.8217
 
0.7%
27.2616
 
0.6%
17.6815
 
0.6%
21.4315
 
0.6%
23.2114
 
0.5%
23.5713
 
0.5%
16.5513
 
0.5%
Other values (176)312
 
12.2%
(Missing)666
26.0%
ValueCountFrequency (%)
01390
54.3%
4.341
 
< 0.1%
9.964
 
0.2%
10.581
 
< 0.1%
11.896
 
0.2%
ValueCountFrequency (%)
68.751
< 0.1%
55.761
< 0.1%
50.051
< 0.1%
47.51
< 0.1%
47.291
< 0.1%

P1_DOB
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1768
Distinct (%)93.3%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
12/12/1939
 
3
02/07/1931
 
3
25/05/1928
 
3
14/08/1927
 
3
01/01/1920
 
2
Other values (1763)
1881 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters18950
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1645 ?
Unique (%)86.8%

Sample

1st row29/10/1943
2nd row06/07/1943
3rd row03/05/1936
4th row16/02/1953
5th row06/06/1930
ValueCountFrequency (%)
12/12/19393
 
0.1%
02/07/19313
 
0.1%
25/05/19283
 
0.1%
14/08/19273
 
0.1%
01/01/19202
 
0.1%
06/08/19332
 
0.1%
24/12/19342
 
0.1%
07/06/19432
 
0.1%
22/04/19492
 
0.1%
08/08/19212
 
0.1%
Other values (1758)1871
73.1%
(Missing)666
 
26.0%
2021-01-25T20:53:54.580669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/12/19393
 
0.2%
25/05/19283
 
0.2%
02/07/19313
 
0.2%
14/08/19273
 
0.2%
01/08/19392
 
0.1%
29/11/19332
 
0.1%
28/02/19502
 
0.1%
26/05/19342
 
0.1%
04/09/19442
 
0.1%
15/08/19302
 
0.1%
Other values (1758)1871
98.7%

Most occurring characters

ValueCountFrequency (%)
/3790
20.0%
13768
19.9%
02529
13.3%
92374
12.5%
21675
8.8%
31257
 
6.6%
41096
 
5.8%
5781
 
4.1%
6588
 
3.1%
7580
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15160
80.0%
Other Punctuation3790
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
13768
24.9%
02529
16.7%
92374
15.7%
21675
11.0%
31257
 
8.3%
41096
 
7.2%
5781
 
5.2%
6588
 
3.9%
7580
 
3.8%
8512
 
3.4%
ValueCountFrequency (%)
/3790
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18950
100.0%

Most frequent character per script

ValueCountFrequency (%)
/3790
20.0%
13768
19.9%
02529
13.3%
92374
12.5%
21675
8.8%
31257
 
6.6%
41096
 
5.8%
5781
 
4.1%
6588
 
3.1%
7580
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII18950
100.0%

Most frequent character per block

ValueCountFrequency (%)
/3790
20.0%
13768
19.9%
02529
13.3%
92374
12.5%
21675
8.8%
31257
 
6.6%
41096
 
5.8%
5781
 
4.1%
6588
 
3.1%
7580
 
3.1%

P1_MAR_STATUS
Categorical

MISSING

Distinct9
Distinct (%)0.5%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
M
652 
P
457 
O
324 
W
261 
S
106 
Other values (4)
95 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1895
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowM
3rd rowM
4th rowM
5th rowO
ValueCountFrequency (%)
M652
25.5%
P457
17.8%
O324
12.7%
W261
 
10.2%
S106
 
4.1%
D77
 
3.0%
A13
 
0.5%
C3
 
0.1%
B2
 
0.1%
(Missing)666
26.0%
2021-01-25T20:53:54.953691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:55.038695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
m652
34.4%
p457
24.1%
o324
17.1%
w261
13.8%
s106
 
5.6%
d77
 
4.1%
a13
 
0.7%
c3
 
0.2%
b2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
M652
34.4%
P457
24.1%
O324
17.1%
W261
13.8%
S106
 
5.6%
D77
 
4.1%
A13
 
0.7%
C3
 
0.2%
B2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1895
100.0%

Most frequent character per category

ValueCountFrequency (%)
M652
34.4%
P457
24.1%
O324
17.1%
W261
13.8%
S106
 
5.6%
D77
 
4.1%
A13
 
0.7%
C3
 
0.2%
B2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1895
100.0%

Most frequent character per script

ValueCountFrequency (%)
M652
34.4%
P457
24.1%
O324
17.1%
W261
13.8%
S106
 
5.6%
D77
 
4.1%
A13
 
0.7%
C3
 
0.2%
B2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1895
100.0%

Most frequent character per block

ValueCountFrequency (%)
M652
34.4%
P457
24.1%
O324
17.1%
W261
13.8%
S106
 
5.6%
D77
 
4.1%
A13
 
0.7%
C3
 
0.2%
B2
 
0.1%

P1_POLICY_REFUSED
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1894 
True
 
1
(Missing)
666 
ValueCountFrequency (%)
False1894
74.0%
True1
 
< 0.1%
(Missing)666
 
26.0%
2021-01-25T20:53:55.122700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

P1_SEX
Categorical

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
M
1005 
F
890 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1895
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowM
5th rowF
ValueCountFrequency (%)
M1005
39.2%
F890
34.8%
(Missing)666
26.0%
2021-01-25T20:53:55.331712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:55.411717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
m1005
53.0%
f890
47.0%

Most occurring characters

ValueCountFrequency (%)
M1005
53.0%
F890
47.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1895
100.0%

Most frequent character per category

ValueCountFrequency (%)
M1005
53.0%
F890
47.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1895
100.0%

Most frequent character per script

ValueCountFrequency (%)
M1005
53.0%
F890
47.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1895
100.0%

Most frequent character per block

ValueCountFrequency (%)
M1005
53.0%
F890
47.0%

APPR_ALARM
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1761 
True
 
134
(Missing)
666 
ValueCountFrequency (%)
False1761
68.8%
True134
 
5.2%
(Missing)666
 
26.0%
2021-01-25T20:53:55.455719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

APPR_LOCKS
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1354 
False
541 
(Missing)
666 
ValueCountFrequency (%)
True1354
52.9%
False541
 
21.1%
(Missing)666
26.0%
2021-01-25T20:53:55.500722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

BEDROOMS
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)0.3%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean2.786807388
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:55.575726image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8181772646
Coefficient of variation (CV)0.2935894559
Kurtosis0.3719131453
Mean2.786807388
Median Absolute Deviation (MAD)0
Skewness0.00507977937
Sum5281
Variance0.6694140363
MonotocityNot monotonic
2021-01-25T20:53:55.690733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3983
38.4%
2511
20.0%
4257
 
10.0%
1110
 
4.3%
532
 
1.2%
62
 
0.1%
(Missing)666
26.0%
ValueCountFrequency (%)
1110
 
4.3%
2511
20.0%
3983
38.4%
4257
 
10.0%
532
 
1.2%
ValueCountFrequency (%)
62
 
0.1%
532
 
1.2%
4257
 
10.0%
3983
38.4%
2511
20.0%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
11.0
1891 
nan
666 
19.0
 
3
2.0
 
1

Length

Max length4
Median length4
Mean length3.739554861
Min length3

Characters and Unicode

Total characters9577
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd row11.0
3rd row11.0
4th row11.0
5th row11.0
ValueCountFrequency (%)
11.01891
73.8%
nan666
 
26.0%
19.03
 
0.1%
2.01
 
< 0.1%
2021-01-25T20:53:55.959748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:56.040753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
11.01891
73.8%
nan666
 
26.0%
19.03
 
0.1%
2.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
13785
39.5%
.1895
19.8%
01895
19.8%
n1332
 
13.9%
a666
 
7.0%
93
 
< 0.1%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5684
59.4%
Lowercase Letter1998
 
20.9%
Other Punctuation1895
 
19.8%

Most frequent character per category

ValueCountFrequency (%)
13785
66.6%
01895
33.3%
93
 
0.1%
21
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%
ValueCountFrequency (%)
.1895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7579
79.1%
Latin1998
 
20.9%

Most frequent character per script

ValueCountFrequency (%)
13785
49.9%
.1895
25.0%
01895
25.0%
93
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII9577
100.0%

Most frequent character per block

ValueCountFrequency (%)
13785
39.5%
.1895
19.8%
01895
19.8%
n1332
 
13.9%
a666
 
7.0%
93
 
< 0.1%
21
 
< 0.1%

WALL_CONSTRUCTION
Real number (ℝ≥0)

MISSING
SKEWED

Distinct6
Distinct (%)0.3%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean14.98944591
Minimum3
Maximum20
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:56.132758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile15
Q115
median15
Q315
95-th percentile15
Maximum20
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.427282935
Coefficient of variation (CV)0.0285055857
Kurtosis671.8517803
Mean14.98944591
Median Absolute Deviation (MAD)0
Skewness-22.50128421
Sum28405
Variance0.1825707066
MonotocityNot monotonic
2021-01-25T20:53:56.241764image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
151889
73.8%
32
 
0.1%
201
 
< 0.1%
191
 
< 0.1%
111
 
< 0.1%
141
 
< 0.1%
(Missing)666
 
26.0%
ValueCountFrequency (%)
32
 
0.1%
111
 
< 0.1%
141
 
< 0.1%
151889
73.8%
191
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
191
 
< 0.1%
151889
73.8%
141
 
< 0.1%
111
 
< 0.1%

FLOODING
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1851 
False
 
44
(Missing)
666 
ValueCountFrequency (%)
True1851
72.3%
False44
 
1.7%
(Missing)666
 
26.0%
2021-01-25T20:53:56.326769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

LISTED
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
3.0
1886 
nan
666 
2.0
 
7
1.0
 
1
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7683
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rownan
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
3.01886
73.6%
nan666
 
26.0%
2.07
 
0.3%
1.01
 
< 0.1%
5.01
 
< 0.1%
2021-01-25T20:53:56.528781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:56.602785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
3.01886
73.6%
nan666
 
26.0%
2.07
 
0.3%
1.01
 
< 0.1%
5.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.1895
24.7%
01895
24.7%
31886
24.5%
n1332
17.3%
a666
 
8.7%
27
 
0.1%
51
 
< 0.1%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3790
49.3%
Lowercase Letter1998
26.0%
Other Punctuation1895
24.7%

Most frequent character per category

ValueCountFrequency (%)
01895
50.0%
31886
49.8%
27
 
0.2%
51
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%
ValueCountFrequency (%)
.1895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5685
74.0%
Latin1998
 
26.0%

Most frequent character per script

ValueCountFrequency (%)
.1895
33.3%
01895
33.3%
31886
33.2%
27
 
0.1%
51
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7683
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1895
24.7%
01895
24.7%
31886
24.5%
n1332
17.3%
a666
 
8.7%
27
 
0.1%
51
 
< 0.1%
11
 
< 0.1%

MAX_DAYS_UNOCC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0.0
1402 
nan
666 
30.0
492 
90.0
 
1

Length

Max length4
Median length3
Mean length3.192502929
Min length3

Characters and Unicode

Total characters8176
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd row30.0
3rd row30.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01402
54.7%
nan666
26.0%
30.0492
 
19.2%
90.01
 
< 0.1%
2021-01-25T20:53:56.833798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:56.919803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.01402
54.7%
nan666
26.0%
30.0492
 
19.2%
90.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
03790
46.4%
.1895
23.2%
n1332
 
16.3%
a666
 
8.1%
3492
 
6.0%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4283
52.4%
Lowercase Letter1998
24.4%
Other Punctuation1895
23.2%

Most frequent character per category

ValueCountFrequency (%)
03790
88.5%
3492
 
11.5%
91
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%
ValueCountFrequency (%)
.1895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6178
75.6%
Latin1998
 
24.4%

Most frequent character per script

ValueCountFrequency (%)
03790
61.3%
.1895
30.7%
3492
 
8.0%
91
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8176
100.0%

Most frequent character per block

ValueCountFrequency (%)
03790
46.4%
.1895
23.2%
n1332
 
16.3%
a666
 
8.1%
3492
 
6.0%
91
 
< 0.1%

NEIGH_WATCH
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1433 
True
462 
(Missing)
666 
ValueCountFrequency (%)
False1433
56.0%
True462
 
18.0%
(Missing)666
26.0%
2021-01-25T20:53:56.975806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

OCC_STATUS
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
PH
1886 
LP
 
7
HH
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3790
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPH
2nd rowPH
3rd rowPH
4th rowPH
5th rowPH
ValueCountFrequency (%)
PH1886
73.6%
LP7
 
0.3%
HH2
 
0.1%
(Missing)666
 
26.0%
2021-01-25T20:53:57.191818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:57.267823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ph1886
99.5%
lp7
 
0.4%
hh2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
P1893
49.9%
H1890
49.9%
L7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3790
100.0%

Most frequent character per category

ValueCountFrequency (%)
P1893
49.9%
H1890
49.9%
L7
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3790
100.0%

Most frequent character per script

ValueCountFrequency (%)
P1893
49.9%
H1890
49.9%
L7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3790
100.0%

Most frequent character per block

ValueCountFrequency (%)
P1893
49.9%
H1890
49.9%
L7
 
0.2%

OWNERSHIP_TYPE
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)0.5%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean7.600527704
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:57.338827image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median8
Q38
95-th percentile12
Maximum18
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.467165528
Coefficient of variation (CV)0.3246045043
Kurtosis4.776959056
Mean7.600527704
Median Absolute Deviation (MAD)0
Skewness0.6057756202
Sum14403
Variance6.08690574
MonotocityNot monotonic
2021-01-25T20:53:57.442833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
81507
58.8%
3275
 
10.7%
1248
 
1.9%
1830
 
1.2%
1424
 
0.9%
26
 
0.2%
112
 
0.1%
131
 
< 0.1%
161
 
< 0.1%
71
 
< 0.1%
(Missing)666
26.0%
ValueCountFrequency (%)
26
 
0.2%
3275
 
10.7%
71
 
< 0.1%
81507
58.8%
112
 
0.1%
ValueCountFrequency (%)
1830
1.2%
161
 
< 0.1%
1424
0.9%
131
 
< 0.1%
1248
1.9%

PAYING_GUESTS
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
0.0
1894 
nan
666 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7683
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rownan
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01894
74.0%
nan666
 
26.0%
1.01
 
< 0.1%
2021-01-25T20:53:57.692847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:57.769852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0.01894
74.0%
nan666
 
26.0%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
03789
49.3%
.1895
24.7%
n1332
 
17.3%
a666
 
8.7%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3790
49.3%
Lowercase Letter1998
26.0%
Other Punctuation1895
24.7%

Most frequent character per category

ValueCountFrequency (%)
n1332
66.7%
a666
33.3%
ValueCountFrequency (%)
03789
> 99.9%
11
 
< 0.1%
ValueCountFrequency (%)
.1895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5685
74.0%
Latin1998
 
26.0%

Most frequent character per script

ValueCountFrequency (%)
03789
66.6%
.1895
33.3%
11
 
< 0.1%
ValueCountFrequency (%)
n1332
66.7%
a666
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7683
100.0%

Most frequent character per block

ValueCountFrequency (%)
03789
49.3%
.1895
24.7%
n1332
 
17.3%
a666
 
8.7%
11
 
< 0.1%

PROP_TYPE
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)1.2%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean10.02005277
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:57.848856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q317
95-th percentile25
Maximum53
Range52
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.092892602
Coefficient of variation (CV)0.9074695324
Kurtosis4.533075604
Mean10.02005277
Median Absolute Deviation (MAD)8
Skewness1.687254796
Sum18988
Variance82.68069588
MonotocityNot monotonic
2021-01-25T20:53:57.976863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10538
21.0%
2345
13.5%
1318
12.4%
19265
 
10.3%
9151
 
5.9%
1865
 
2.5%
2564
 
2.5%
750
 
2.0%
2629
 
1.1%
4817
 
0.7%
Other values (13)53
 
2.1%
(Missing)666
26.0%
ValueCountFrequency (%)
1318
12.4%
2345
13.5%
31
 
< 0.1%
411
 
0.4%
750
 
2.0%
ValueCountFrequency (%)
532
 
0.1%
521
 
< 0.1%
5110
0.4%
4817
0.7%
473
 
0.1%

SAFE_INSTALLED
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1879 
True
 
16
(Missing)
666 
ValueCountFrequency (%)
False1879
73.4%
True16
 
0.6%
(Missing)666
 
26.0%
2021-01-25T20:53:58.061868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

SEC_DISC_REQ
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1475 
False
420 
(Missing)
666 
ValueCountFrequency (%)
True1475
57.6%
False420
 
16.4%
(Missing)666
26.0%
2021-01-25T20:53:58.103871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

SUBSIDENCE
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1879 
True
 
16
(Missing)
666 
ValueCountFrequency (%)
False1879
73.4%
True16
 
0.6%
(Missing)666
 
26.0%
2021-01-25T20:53:58.145873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

YEARBUILT
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)0.5%
Missing666
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean1946.203694
Minimum1749
Maximum2000
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:53:58.209877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1749
5-th percentile1900
Q11920
median1946
Q31960
95-th percentile1990
Maximum2000
Range251
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.49086419
Coefficient of variation (CV)0.01463919952
Kurtosis1.23269023
Mean1946.203694
Median Absolute Deviation (MAD)14
Skewness-0.6626701394
Sum3688056
Variance811.7293422
MonotocityNot monotonic
2021-01-25T20:53:58.320883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1946666
26.0%
1960314
12.3%
1920309
12.1%
1980298
11.6%
1900144
 
5.6%
199065
 
2.5%
200041
 
1.6%
186929
 
1.1%
187028
 
1.1%
17491
 
< 0.1%
(Missing)666
26.0%
ValueCountFrequency (%)
17491
 
< 0.1%
186929
 
1.1%
187028
 
1.1%
1900144
5.6%
1920309
12.1%
ValueCountFrequency (%)
200041
 
1.6%
199065
 
2.5%
1980298
11.6%
1960314
12.3%
1946666
26.0%

CAMPAIGN_DESC
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2561
Missing (%)100.0%
Memory size20.1 KiB

PAYMENT_METHOD
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
NonDD
955 
PureDD
872 
DD-Other
 
68

Length

Max length8
Median length5
Mean length5.567810026
Min length5

Characters and Unicode

Total characters10551
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNonDD
2nd rowPureDD
3rd rowNonDD
4th rowPureDD
5th rowNonDD
ValueCountFrequency (%)
NonDD955
37.3%
PureDD872
34.0%
DD-Other68
 
2.7%
(Missing)666
26.0%
2021-01-25T20:53:58.546896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:58.626901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nondd955
50.4%
puredd872
46.0%
dd-other68
 
3.6%

Most occurring characters

ValueCountFrequency (%)
D3790
35.9%
N955
 
9.1%
o955
 
9.1%
n955
 
9.1%
r940
 
8.9%
e940
 
8.9%
P872
 
8.3%
u872
 
8.3%
-68
 
0.6%
O68
 
0.6%
Other values (2)136
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5685
53.9%
Lowercase Letter4798
45.5%
Dash Punctuation68
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
o955
19.9%
n955
19.9%
r940
19.6%
e940
19.6%
u872
18.2%
t68
 
1.4%
h68
 
1.4%
ValueCountFrequency (%)
D3790
66.7%
N955
 
16.8%
P872
 
15.3%
O68
 
1.2%
ValueCountFrequency (%)
-68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10483
99.4%
Common68
 
0.6%

Most frequent character per script

ValueCountFrequency (%)
D3790
36.2%
N955
 
9.1%
o955
 
9.1%
n955
 
9.1%
r940
 
9.0%
e940
 
9.0%
P872
 
8.3%
u872
 
8.3%
O68
 
0.6%
t68
 
0.6%
ValueCountFrequency (%)
-68
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10551
100.0%

Most frequent character per block

ValueCountFrequency (%)
D3790
35.9%
N955
 
9.1%
o955
 
9.1%
n955
 
9.1%
r940
 
8.9%
e940
 
8.9%
P872
 
8.3%
u872
 
8.3%
-68
 
0.6%
O68
 
0.6%
Other values (2)136
 
1.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
nan
1733 
1.0
828 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7683
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th row1.0
5th rownan
ValueCountFrequency (%)
nan1733
67.7%
1.0828
32.3%
2021-01-25T20:53:58.811911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:53:59.279938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nan1733
67.7%
1.0828
32.3%

Most occurring characters

ValueCountFrequency (%)
n3466
45.1%
a1733
22.6%
1828
 
10.8%
.828
 
10.8%
0828
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5199
67.7%
Decimal Number1656
 
21.6%
Other Punctuation828
 
10.8%

Most frequent character per category

ValueCountFrequency (%)
n3466
66.7%
a1733
33.3%
ValueCountFrequency (%)
1828
50.0%
0828
50.0%
ValueCountFrequency (%)
.828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5199
67.7%
Common2484
32.3%

Most frequent character per script

ValueCountFrequency (%)
1828
33.3%
.828
33.3%
0828
33.3%
ValueCountFrequency (%)
n3466
66.7%
a1733
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7683
100.0%

Most frequent character per block

ValueCountFrequency (%)
n3466
45.1%
a1733
22.6%
1828
 
10.8%
.828
 
10.8%
0828
 
10.8%

LEGAL_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1129 
False
766 
(Missing)
666 
ValueCountFrequency (%)
True1129
44.1%
False766
29.9%
(Missing)666
26.0%
2021-01-25T20:53:59.322940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

LEGAL_ADDON_POST_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
True
1042 
False
853 
(Missing)
666 
ValueCountFrequency (%)
True1042
40.7%
False853
33.3%
(Missing)666
26.0%
2021-01-25T20:53:59.365943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HOME_EM_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1397 
True
498 
(Missing)
666 
ValueCountFrequency (%)
False1397
54.5%
True498
 
19.4%
(Missing)666
26.0%
2021-01-25T20:53:59.407945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1819 
True
 
76
(Missing)
666 
ValueCountFrequency (%)
False1819
71.0%
True76
 
3.0%
(Missing)666
 
26.0%
2021-01-25T20:53:59.449948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

GARDEN_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1727 
True
 
168
(Missing)
666 
ValueCountFrequency (%)
False1727
67.4%
True168
 
6.6%
(Missing)666
 
26.0%
2021-01-25T20:53:59.490950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

GARDEN_ADDON_POST_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1736 
True
 
159
(Missing)
666 
ValueCountFrequency (%)
False1736
67.8%
True159
 
6.2%
(Missing)666
 
26.0%
2021-01-25T20:53:59.532953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

KEYCARE_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1797 
True
 
98
(Missing)
666 
ValueCountFrequency (%)
False1797
70.2%
True98
 
3.8%
(Missing)666
 
26.0%
2021-01-25T20:53:59.573955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1787 
True
 
108
(Missing)
666 
ValueCountFrequency (%)
False1787
69.8%
True108
 
4.2%
(Missing)666
 
26.0%
2021-01-25T20:53:59.628958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP1_ADDON_PRE_REN
Boolean

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1895 
(Missing)
666 
ValueCountFrequency (%)
False1895
74.0%
(Missing)666
 
26.0%
2021-01-25T20:53:59.679961image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP1_ADDON_POST_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1868 
True
 
27
(Missing)
666 
ValueCountFrequency (%)
False1868
72.9%
True27
 
1.1%
(Missing)666
 
26.0%
2021-01-25T20:53:59.716963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP2_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1893 
True
 
2
(Missing)
666 
ValueCountFrequency (%)
False1893
73.9%
True2
 
0.1%
(Missing)666
 
26.0%
2021-01-25T20:53:59.758965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP2_ADDON_POST_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1558 
True
337 
(Missing)
666 
ValueCountFrequency (%)
False1558
60.8%
True337
 
13.2%
(Missing)666
26.0%
2021-01-25T20:53:59.800968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP3_ADDON_PRE_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1894 
True
 
1
(Missing)
666 
ValueCountFrequency (%)
False1894
74.0%
True1
 
< 0.1%
(Missing)666
 
26.0%
2021-01-25T20:53:59.844970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

HP3_ADDON_POST_REN
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing666
Missing (%)26.0%
Memory size5.1 KiB
False
1892 
True
 
3
(Missing)
666 
ValueCountFrequency (%)
False1892
73.9%
True3
 
0.1%
(Missing)666
 
26.0%
2021-01-25T20:53:59.887973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

MTA_FLAG
Boolean

MISSING

Distinct2
Distinct (%)0.1%
Missing659
Missing (%)25.7%
Memory size5.1 KiB
False
1323 
True
579 
(Missing)
659 
ValueCountFrequency (%)
False1323
51.7%
True579
22.6%
(Missing)659
25.7%
2021-01-25T20:53:59.933975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

MTA_FAP
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct559
Distinct (%)96.5%
Missing1982
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean204.6248014
Minimum-26.36
Maximum1247.47
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:54:00.028981image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-26.36
5-th percentile51.94
Q1137.315
median195.38
Q3253.84
95-th percentile380.791
Maximum1247.47
Range1273.83
Interquartile range (IQR)116.525

Descriptive statistics

Standard deviation111.2112037
Coefficient of variation (CV)0.5434883894
Kurtosis15.25507096
Mean204.6248014
Median Absolute Deviation (MAD)58.13
Skewness2.322912032
Sum118477.76
Variance12367.93184
MonotocityNot monotonic
2021-01-25T20:54:00.193990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4511
 
0.4%
51.945
 
0.2%
168.682
 
0.1%
198.012
 
0.1%
315.062
 
0.1%
54.752
 
0.1%
157.032
 
0.1%
215.552
 
0.1%
230.111
 
< 0.1%
318.031
 
< 0.1%
Other values (549)549
 
21.4%
(Missing)1982
77.4%
ValueCountFrequency (%)
-26.361
< 0.1%
40.011
< 0.1%
41.161
< 0.1%
42.951
< 0.1%
43.881
< 0.1%
ValueCountFrequency (%)
1247.471
< 0.1%
772.541
< 0.1%
705.611
< 0.1%
699.811
< 0.1%
601.481
< 0.1%

MTA_APRP
Real number (ℝ)

MISSING
ZEROS

Distinct296
Distinct (%)51.1%
Missing1982
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean89.91554404
Minimum-183.47
Maximum1247.47
Zeros274
Zeros (%)10.7%
Memory size20.1 KiB
2021-01-25T20:54:00.355999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-183.47
5-th percentile-0.279
Q10
median0
Q3180.425
95-th percentile313.269
Maximum1247.47
Range1430.94
Interquartile range (IQR)180.425

Descriptive statistics

Standard deviation130.1781154
Coefficient of variation (CV)1.447782103
Kurtosis10.95599992
Mean89.91554404
Median Absolute Deviation (MAD)4.28
Skewness2.134731291
Sum52061.1
Variance16946.34173
MonotocityNot monotonic
2021-01-25T20:54:00.545010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0274
 
10.7%
51.456
 
0.2%
51.943
 
0.1%
215.552
 
0.1%
-0.362
 
0.1%
315.062
 
0.1%
271.791
 
< 0.1%
120.981
 
< 0.1%
212.181
 
< 0.1%
84.341
 
< 0.1%
Other values (286)286
 
11.2%
(Missing)1982
77.4%
ValueCountFrequency (%)
-183.471
< 0.1%
-122.571
< 0.1%
-41.671
< 0.1%
-38.121
< 0.1%
-36.711
< 0.1%
ValueCountFrequency (%)
1247.471
< 0.1%
705.611
< 0.1%
560.311
< 0.1%
555.431
< 0.1%
467.181
< 0.1%

MTA_DATE
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct205
Distinct (%)79.2%
Missing2302
Missing (%)89.9%
Memory size20.1 KiB
01/02/2010
 
4
09/01/2010
 
3
04/02/2010
 
3
11/01/2010
 
3
01/03/2010
 
3
Other values (200)
243 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2590
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)62.2%

Sample

1st row26/01/2010
2nd row03/03/2010
3rd row24/11/2009
4th row08/11/2010
5th row26/05/2010
ValueCountFrequency (%)
01/02/20104
 
0.2%
09/01/20103
 
0.1%
04/02/20103
 
0.1%
11/01/20103
 
0.1%
01/03/20103
 
0.1%
23/11/20093
 
0.1%
10/02/20103
 
0.1%
20/01/20103
 
0.1%
20/02/20103
 
0.1%
22/04/20102
 
0.1%
Other values (195)229
 
8.9%
(Missing)2302
89.9%
2021-01-25T20:54:00.872029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/02/20104
 
1.5%
11/01/20103
 
1.2%
20/02/20103
 
1.2%
23/11/20093
 
1.2%
20/01/20103
 
1.2%
04/02/20103
 
1.2%
09/01/20103
 
1.2%
10/02/20103
 
1.2%
01/03/20103
 
1.2%
02/03/20102
 
0.8%
Other values (195)229
88.4%

Most occurring characters

ValueCountFrequency (%)
0809
31.2%
/518
20.0%
1461
17.8%
2435
16.8%
9106
 
4.1%
364
 
2.5%
848
 
1.9%
640
 
1.5%
737
 
1.4%
436
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2072
80.0%
Other Punctuation518
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0809
39.0%
1461
22.2%
2435
21.0%
9106
 
5.1%
364
 
3.1%
848
 
2.3%
640
 
1.9%
737
 
1.8%
436
 
1.7%
536
 
1.7%
ValueCountFrequency (%)
/518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2590
100.0%

Most frequent character per script

ValueCountFrequency (%)
0809
31.2%
/518
20.0%
1461
17.8%
2435
16.8%
9106
 
4.1%
364
 
2.5%
848
 
1.9%
640
 
1.5%
737
 
1.4%
436
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2590
100.0%

Most frequent character per block

ValueCountFrequency (%)
0809
31.2%
/518
20.0%
1461
17.8%
2435
16.8%
9106
 
4.1%
364
 
2.5%
848
 
1.9%
640
 
1.5%
737
 
1.4%
436
 
1.4%

LAST_ANN_PREM_GROSS
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1739
Distinct (%)91.4%
Missing659
Missing (%)25.7%
Infinite0
Infinite (%)0.0%
Mean188.3664826
Minimum-26.36
Maximum1247.47
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:54:01.009037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-26.36
5-th percentile51.94
Q1124.815
median179.735
Q3237.7275
95-th percentile361.1025
Maximum1247.47
Range1273.83
Interquartile range (IQR)112.9125

Descriptive statistics

Standard deviation99.9075918
Coefficient of variation (CV)0.5303894323
Kurtosis8.975209243
Mean188.3664826
Median Absolute Deviation (MAD)56.52
Skewness1.635408979
Sum358273.05
Variance9981.5269
MonotocityNot monotonic
2021-01-25T20:54:01.170046image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.4541
 
1.6%
51.9428
 
1.1%
54.449
 
0.4%
52.636
 
0.2%
52.134
 
0.2%
54.754
 
0.2%
56.874
 
0.2%
55.513
 
0.1%
2003
 
0.1%
53.693
 
0.1%
Other values (1729)1797
70.2%
(Missing)659
 
25.7%
ValueCountFrequency (%)
-26.361
< 0.1%
40.011
< 0.1%
40.251
< 0.1%
41.162
0.1%
41.551
< 0.1%
ValueCountFrequency (%)
1247.471
< 0.1%
772.541
< 0.1%
729.541
< 0.1%
729.31
< 0.1%
705.611
< 0.1%

POL_STATUS
Categorical

MISSING

Distinct3
Distinct (%)0.2%
Missing666
Missing (%)26.0%
Memory size20.1 KiB
Live
1305 
Lapsed
538 
Cancelled
 
52

Length

Max length9
Median length4
Mean length4.705013193
Min length4

Characters and Unicode

Total characters8916
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLive
2nd rowLive
3rd rowLive
4th rowLapsed
5th rowLive
ValueCountFrequency (%)
Live1305
51.0%
Lapsed538
21.0%
Cancelled52
 
2.0%
(Missing)666
26.0%
2021-01-25T20:54:01.433061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-25T20:54:01.508065image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
live1305
68.9%
lapsed538
28.4%
cancelled52
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e1947
21.8%
L1843
20.7%
i1305
14.6%
v1305
14.6%
a590
 
6.6%
d590
 
6.6%
p538
 
6.0%
s538
 
6.0%
l104
 
1.2%
C52
 
0.6%
Other values (2)104
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7021
78.7%
Uppercase Letter1895
 
21.3%

Most frequent character per category

ValueCountFrequency (%)
e1947
27.7%
i1305
18.6%
v1305
18.6%
a590
 
8.4%
d590
 
8.4%
p538
 
7.7%
s538
 
7.7%
l104
 
1.5%
n52
 
0.7%
c52
 
0.7%
ValueCountFrequency (%)
L1843
97.3%
C52
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin8916
100.0%

Most frequent character per script

ValueCountFrequency (%)
e1947
21.8%
L1843
20.7%
i1305
14.6%
v1305
14.6%
a590
 
6.6%
d590
 
6.6%
p538
 
6.0%
s538
 
6.0%
l104
 
1.2%
C52
 
0.6%
Other values (2)104
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII8916
100.0%

Most frequent character per block

ValueCountFrequency (%)
e1947
21.8%
L1843
20.7%
i1305
14.6%
v1305
14.6%
a590
 
6.6%
d590
 
6.6%
p538
 
6.0%
s538
 
6.0%
l104
 
1.2%
C52
 
0.6%
Other values (2)104
 
1.2%

i
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2561
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129830.3581
Minimum33
Maximum256113
Zeros0
Zeros (%)0.0%
Memory size20.1 KiB
2021-01-25T20:54:01.627072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile12463
Q166922
median129826
Q3194810
95-th percentile243394
Maximum256113
Range256080
Interquartile range (IQR)127888

Descriptive statistics

Standard deviation74214.84674
Coefficient of variation (CV)0.5716293774
Kurtosis-1.19492662
Mean129830.3581
Median Absolute Deviation (MAD)63944
Skewness-0.0282409952
Sum332495547
Variance5507843477
MonotocityNot monotonic
2021-01-25T20:54:01.795082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2478091
 
< 0.1%
1877211
 
< 0.1%
149381
 
< 0.1%
641921
 
< 0.1%
273301
 
< 0.1%
1904261
 
< 0.1%
2177971
 
< 0.1%
1092541
 
< 0.1%
2026301
 
< 0.1%
2198481
 
< 0.1%
Other values (2551)2551
99.6%
ValueCountFrequency (%)
331
< 0.1%
1401
< 0.1%
2001
< 0.1%
2601
< 0.1%
2811
< 0.1%
ValueCountFrequency (%)
2561131
< 0.1%
2560921
< 0.1%
2559841
< 0.1%
2559621
< 0.1%
2558921
< 0.1%

Police
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2561
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
P043875
 
1
P110481
 
1
P022580
 
1
P207732
 
1
P094268
 
1
Other values (2556)
2556 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters17927
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2561 ?
Unique (%)100.0%

Sample

1st rowP102224
2nd rowP058319
3rd rowP143989
4th rowP157076
5th rowP210969
ValueCountFrequency (%)
P0438751
 
< 0.1%
P1104811
 
< 0.1%
P0225801
 
< 0.1%
P2077321
 
< 0.1%
P0942681
 
< 0.1%
P0365551
 
< 0.1%
P0223341
 
< 0.1%
P1046611
 
< 0.1%
P0701981
 
< 0.1%
P2546311
 
< 0.1%
Other values (2551)2551
99.6%
2021-01-25T20:54:02.118100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p2398991
 
< 0.1%
p0510911
 
< 0.1%
p2511291
 
< 0.1%
p1818061
 
< 0.1%
p2127811
 
< 0.1%
p0131621
 
< 0.1%
p1743391
 
< 0.1%
p0731151
 
< 0.1%
p2294891
 
< 0.1%
p1604421
 
< 0.1%
Other values (2551)2551
99.6%

Most occurring characters

ValueCountFrequency (%)
P2561
14.3%
12294
12.8%
02272
12.7%
21851
10.3%
31388
7.7%
41343
7.5%
51283
7.2%
81267
7.1%
91232
6.9%
71223
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number15366
85.7%
Uppercase Letter2561
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
12294
14.9%
02272
14.8%
21851
12.0%
31388
9.0%
41343
8.7%
51283
8.3%
81267
8.2%
91232
8.0%
71223
8.0%
61213
7.9%
ValueCountFrequency (%)
P2561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15366
85.7%
Latin2561
 
14.3%

Most frequent character per script

ValueCountFrequency (%)
12294
14.9%
02272
14.8%
21851
12.0%
31388
9.0%
41343
8.7%
51283
8.3%
81267
8.2%
91232
8.0%
71223
8.0%
61213
7.9%
ValueCountFrequency (%)
P2561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17927
100.0%

Most frequent character per block

ValueCountFrequency (%)
P2561
14.3%
12294
12.8%
02272
12.7%
21851
10.3%
31388
7.7%
41343
7.5%
51283
7.2%
81267
7.1%
91232
6.9%
71223
6.8%

Interactions

2021-01-25T20:52:57.148385image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.307393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.470403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.601410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.728418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.852425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:57.984432image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.119440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.251447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.377455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.511462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.642470image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.767477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:58.893484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:52:59.184501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:59.312508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:52:59.438515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:52:59.914543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.054550image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.180558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.317566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.460574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.591581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.716588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:00.940601image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:53:02.103668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:53:03.853768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:03.981775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:04.151785image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:53:05.164843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:05.319852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:05.461860image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:05.605868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:05.750876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:05.896885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:06.038893image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:06.174901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:53:06.474918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-01-25T20:53:08.521035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-25T20:53:08.661043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
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Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
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The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
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The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexQUOTE_DATECOVER_STARTCLAIM3YEARSP1_EMP_STATUSP1_PT_EMP_STATUSBUS_USECLERICALAD_BUILDINGSRISK_RATED_AREA_BSUM_INSURED_BUILDINGSNCD_GRANTED_YEARS_BAD_CONTENTSRISK_RATED_AREA_CSUM_INSURED_CONTENTSNCD_GRANTED_YEARS_CCONTENTS_COVERBUILDINGS_COVERSPEC_SUM_INSUREDSPEC_ITEM_PREMUNSPEC_HRP_PREMP1_DOBP1_MAR_STATUSP1_POLICY_REFUSEDP1_SEXAPPR_ALARMAPPR_LOCKSBEDROOMSROOF_CONSTRUCTIONWALL_CONSTRUCTIONFLOODINGLISTEDMAX_DAYS_UNOCCNEIGH_WATCHOCC_STATUSOWNERSHIP_TYPEPAYING_GUESTSPROP_TYPESAFE_INSTALLEDSEC_DISC_REQSUBSIDENCEYEARBUILTCAMPAIGN_DESCPAYMENT_METHODPAYMENT_FREQUENCYLEGAL_ADDON_PRE_RENLEGAL_ADDON_POST_RENHOME_EM_ADDON_PRE_RENHOME_EM_ADDON_POST_RENGARDEN_ADDON_PRE_RENGARDEN_ADDON_POST_RENKEYCARE_ADDON_PRE_RENKEYCARE_ADDON_POST_RENHP1_ADDON_PRE_RENHP1_ADDON_POST_RENHP2_ADDON_PRE_RENHP2_ADDON_POST_RENHP3_ADDON_PRE_RENHP3_ADDON_POST_RENMTA_FLAGMTA_FAPMTA_APRPMTA_DATELAST_ANN_PREM_GROSSPOL_STATUSiPolice
010222311/5/2011NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN102224P102224
1583181/6/201010/01/2010NRNaNNNaNY5.01000000.07.0NNaN0.00.0YN0.00.000.0029/10/1943DNMNN4.011.015.0Y3.030.0NPH8.00.018.0NNN1960.0NaNNonDDNaNYYNNNNNNNYNNNNNNaNNaNNaN132.91Live58319P058319
21439881/11/201115/01/2011NRNaNNNaNY11.01000000.06.0Y17.050000.06.0YY0.00.000.0006/07/1943MNFNY3.011.015.0Y3.030.0NPH8.00.010.0NYN1920.0NaNPureDDNaNYYNNNNYYNNNNNNNNaNNaNNaN155.34Live143989P143989
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